Michael Bernstein

Empirical evidence that people can build highly predictive mental models of even complex AIs, if and only if the algorithm fulfills three criteria:

CSCW folks, I wanted to highlight how excited and proud I am to see work from our community (dl.acm.org/doi/10.1145/..., CSCW '24 best paper winner led by @jiachenyan.bsky.social and @mlam.bsky.social) grow and expand ambition into this Science paper. CSCW has a ton to offer the world.

This was such a cool experiment that I created a Zentropi labeler with a simplified version of the authors' Partisan Animosity criteria. Now anyone can experiment directly with using this labeler to try to reduce the temperature of affective polarization in their feeds. zentropi.ai/labelers/b30...

Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by more than 2 points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings.

New paper in Science:

In a platform-independent field experiment, we show that reranking content expressing antidemocratic attitudes and partisan animosity in social media feeds alters affective polarization.

🧵

Platform-independent experiments on social media Changing algorithms with artificial intelligence tools can influence partisan animosity

@jennyallen.bsky.social and @jatucker.bsky.social
add a fantastic Perspective piece on the importance of platform-independent experiments on social media's impact on us: science.org/doi/10.1126/...

Strongly agree!

Reranking partisan animosity in algorithmic social media feeds alters affective polarization Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used thi...

Article with @tiziano.bsky.social, @msaveski.bsky.social, @jiachenyan.bsky.social, Jeff Hancock, and Jeanne Tsai

www.science.org/doi/10.1126/...

screenshot of the title and authors of the Science paper that are linked in the next post

Our new article in @science.org enables social media reranking outside of platforms' walled gardens.

We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.

Reranking partisan animosity in algorithmic social media feeds alters affective polarization Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used thi...

Current CASBS fellow Jeanne Tsai & fmr fellow @mbernst.bsky.social among coauthors of NEW ARTICLE @science.org unveiling a new method to reduce exposure to highly partisan social media posts & improve users’ attitudes

Coverage: news.stanford.edu/stories/2025...

Pub: www.science.org/doi/10.1126/...

🚨New WP!🚨

Structured AI Dialogues Can Increase Happiness and Meaning in Life

In a preregistered RCT, four psychology-grounded #AI chatbots improved well-being across several outcomes.

Co-authors: Jonas Schoene, Johannes Eichstaedt, Aadesh Salecha, Sonja Lyubomirsky

🧵👇

Stanford Engineering (@StanfordEng) launched an open-rank faculty search in design. It can place candidates in any department in our School of Engineering: facultypositions.stanford.edu/en-us/job/49...

I'm thrilled to bring in more colleagues at the intersection of engineering+design!

Thank you so much, Ludwig! I hope you’re doing well!

Thank you!

Thank you!!

Thank you!!

Thank you!

Thank you Danaé!

90s kid sitting in front of CRT display giving a thumbs up!

:) Thank you sir!

Thank you Martin!

Thank you!!

Thank you to everyone for your energy and enthusiasm in joining this adventure with me so far!

Thrilled to share that I’ve successfully defended my PhD dissertation and I will be joining MIT as an Assistant Professor starting Fall 2026, with a shared appointment between Sloan and EECS!

I will be recruiting 1-2 PhD students this upcoming cycle. Consider applying to MIT EECS!

Congratulations!!!

Very cool to see this research developed into a policy brief! Impactful work from a superstar team: @joon-s-pk.bsky.social
@cqzou.bsky.social ‬ @aaronshaw.bsky.social @mako.cc Carrie Cai, Meredith Ringel Morris, @robbwiller.bsky.social Percy Liang, @mbernst.bsky.social

Take a 👀 at this policy brief from @stanfordhai.bsky.social on how AI agents can test ideas in social science. Honored to be part of this amazing team: @joon-s-pk.bsky.social @cqzou.bsky.social @aaronshaw.bsky.social @mako.cc Carrie Cai, Meredith Ringel Morris, Percy Liang, @mbernst.bsky.social

A policy brief on what generative AI simulations of people might be good for! (based on work with the amazing team of @joon-s-pk.bsky.social @cqzou.bsky.social @mako.cc @robbwiller.bsky.social @mbernst.bsky.social Merrie Morris, Carrie Cai, and Percy Liang)

📣 Calling all #CHI2025 attendees who work with human participants: Join our panel discussion on #LLM, #simulation, #syntheticdata, and the future of human subjects research on Apr 30 (Wed), 2:10 - 3:40 PM (JP Time)

Post your questions for panelists here: forms.gle/m2mXY3xFafAX...

LLM Social Simulations Are a Promising Research Method Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, resul...

Should we use LLMs 🤖 to simulate human research subjects 🧑? In our new preprint, we argue sims can augment human studies to scale up social science as AI technology accelerates. We identify 5 tractable challenges and argue this is a promising and underused research method 🧪🧵 arxiv.org/abs/2504.02234

This might be the most useful thing I have come across in social media - a personalized feed of academic papers filtered by your follower network! Highly recommend. #academicsky

Thanks @dorazhao.bsky.social for creating this!